Incorporating qualitative information into quantitative estimation via Sequentially Constrained Hamiltonian Monte Carlo sampling
Incorporating qualitative information into quantitative estimation via Sequentially Constrained Hamiltonian Monte Carlo sampling
复制标题
通过顺序约束哈密顿蒙特卡罗采样将定性信息纳入定量估计
DOI：
10.1109/iros.2017.8206336
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
S. Srinivasa
中科院分区：
文献类型：
--
作者：
Daqing Yi;Shushman Choudhury;S. Srinivasa
在人机协作任务中，结合人类提供的定性信息可以大大提高机器人状态估计的鲁棒性和有效性。我们引入了一个算法框架来模拟定性信息的状态和状态之间的定量约束。我们的方法称为顺序约束汉密尔顿蒙特卡罗，将汉密尔顿动力学集成到顺序约束蒙特卡罗采样中。我们能够生成满足任意复杂、非光滑和不连续约束的样本，这反过来又使我们能够支持广泛的定性信息。我们评估我们的方法约束采样定性和定量与几类约束。SCHMC显著优于Metropolis-Hastings算法（一种标准的马尔可夫链蒙特卡罗（MCMC）方法）和汉密尔顿蒙特卡罗（HMC）方法，在采样的精度（满足约束）和近似的质量。与支持类似约束条件的序贯约束蒙特卡罗方法相比，该方法具有更快的收敛速度和更低的参数敏感性。
In human-robot collaborative tasks, incorporating qualitative information provided by humans can greatly enhance the robustness and efficacy of robot state estimation. We introduce an algorithmic framework to model qualitative information as quantitative constraints on and between states. Our approach, named Sequentially Constrained Hamiltonian Monte Carlo, integrates Hamiltonian dynamics into Sequentially Constrained Monte Carlo sampling. We are able to generate samples that satisfy arbitrarily complex, non-smooth and discontinuous constraints, which in turn allows us to support a wide range of qualitative information. We evaluate our approach for constrained sampling qualitatively and quantitatively with several classes of constraints. SCHMC significantly outperforms the Metropolis-Hastings algorithm (a standard Markov Chain Monte Carlo (MCMC) method) and the Hamiltonian Monte Carlo (HMC) method, in terms of both the accuracy of the sampling (for satisfying constraints) and the quality of approximation. Compared to Sequentially Constrained Monte Carlo (SCMC), which supports similar kinds of constraints, our SCHMC approach has faster convergence rates and lower parameter sensitivity.